Machine Learning which was born from pattern recognition and a theory saying machines can learn itself to accomplish a specific task, is now widely expanded among various organizations. Researchers are interested to watch the outcomes collected from the trained models pushed with data. Machine learning models when introduced to new data, they independently adapt it. […]

Machine Learning which was born from pattern recognition and a theory saying machines can learn itself to accomplish a specific task, is now widely expanded among various organizations. Researchers are interested to watch the outcomes collected from the trained models pushed with data. Machine learning models when introduced to new data, they independently adapt it. These models learn from the past computations for providing reliable and accurate decisions or results. This science of learning is not recently discovered but it has got a fresh acceleration in recent years.

Since the ML algorithms were discovered for a long time, it is being applied to solve the most complex computations of real-world. Here are the few examples of ML applications you must have encountered with:

Recommendation engines used on web platform like Amazon, Netflix etc.- applications of ML.

Risk and fraud detection in FinTech or other sectors- the power of ML.

Linguistic rule creation and ML to understand what your consumer is saying over the Facebook platform.

This sudden increase in popularity of the machine learning is the outcome of the factors which are also influencing Data Mining and Bayesian analysis. Factors like massive amount and varieties of data, cheaper processing and storage cost due to cloud services- are providing pace to ML journey. Machine learning market is expected to touch 8.81 billion USD by the end of 2022 with a CAGR of 44.1%. Fraud and risk management based applications will be the biggest contributor to this surge in ML market.

Organizations are leveraging intelligent ML to fight risk exposures. The advanced services and applications are assisting them to avoid, recognize and recover from crucial risk events. The implemented ML algorithms help businesses to find and analyze major risks in their growth for further mitigation. Organizations can make better decisions and strong strategies to deal with such fraudulent scenarios.

Deciding the right price of a product is not an easy task- it can break or make its fame. Regression techniques are used to predict the numeral figures on the basis of existing features. This way, marketers can optimize various aspects of the consumer journey. Regression technique also assists them in sales forecasting and optimizing overall spending. If you are unaware of the art of visualization and prediction then you can join thisData Science Course which will assist you in creating the required algorithm based models and visualize your data in an arranged manner.

The manufacturing industry is famous for the heavy equipment and machines which demands massive capital investment. By implementing ML algorithm in the industrial environment, it will become easy to get warnings about system failure or any other obstacle in their smooth execution. This way, the maintenance department will get prepared with backup solutions or make strategic plans to take care of defected systems making reduced downtime.

This predictive maintenance empowered by ML is not only driving the manufacturing industries soul but also all the sectors using any kind of machines such as in avionics for engines, elevators used in buildings etc. Apart from maintenance, ML allows contextual analysis of logistics data for mitigating supply chain management risks.

Companies were facing a complex situation is addressing each consumer’s behavior. But thanks to ML for making it possible. ML algorithms capture the human input by following their pattern to perform best in customer service. Insurance companies are the best example where ML enables them to offer insurance services based on the customer information. It simply follows a primary rule- better the model, better will be the decisions or predictions.

Businesses these days throw the significant amount of information from multiple sources- pictures, audio, sensors, videos etc. This much amount of data flowing across digital channels paced up the decision-making process by automating data streams to achieve instant data-driven organization decisions. Companies can use ML in its core processes which are data stream directly flows across the connected environment.

Now it’s clear that the era of ML is evolving thrice faster than any other technology. It has expanded its leg in various sectors – finance, banking, manufacturing, supply and distribution, automotive, avionics, healthcare etc. It barely skips any business-oriented sectors across the globe.

While that innovation has given us exciting new products, new capabilities, and new disruption, it has also given us new concepts to learn about and understand. One of those concepts you hear about a lot is machine learning. Basically, machine learning explores the study and construction of algorithms that can learn from and optimize based […]

While that innovation has given us exciting new products, new capabilities, and new disruption, it has also given us new concepts to learn about and understand. One of those concepts you hear about a lot is machine learning.

Basically, machine learning explores the study and construction of algorithms that can learn from and optimize based on data. Basically, it’s a rudimentary form of artificial intelligence (AI) that can help make decisions for you.

So how do companies like Kahuna use that technology to deliver better customer experiences? We’ve created the infographic below to walk you through the process. Please share it with anyone you think will find it useful.

Self driving cars have inculcated deep learning processes that requires the algorithm to identify and learn from the images fed as raw data. Let’s look at how the need for semantic segmentation has evolved. Initial applications of computer vision required the identification of basic elements such as edges(lines and curves) or, gradients. However understanding an […]

Self driving cars have inculcated deep learning processes that requires the algorithm to identify and learn from the images fed as raw data. Let’s look at how the need for semantic segmentation has evolved.

Initial applications of computer vision required the identification of basic elements such as edges(lines and curves) or, gradients. However understanding an image at pixel level came around only with the coining of full-pixel semantic segmentation. It clusters parts of image together which belong to the same object of interest and hence opens the door to numerous applications.

The journey of identifying each pixel or a grouping pixels together assigning a classID has been through the following process:

Image Classification – identify what is present in the image

Object Recognition (and Detection) – identify what is present in the image and where (via a Bounding Box)

Semantic Segmentation – identify what is present in the image and where (by finding all pixels that belongs to it)

so, let’s look at…

What is Semantic Segmentation?

Semantic Segmentation is a classic Computer Vision problem which involves taking as input some raw data (eg., 2D images) and converting them into a mask with regions of interest highlighted. Many use the term full-pixel semantic segmentation, where each pixel in an image is assigned a classID depending on which object of interest it belongs to.

Earlier computer vision problems only found elements such as edges (lines and curves) or gradients, but they never quite provided an understanding of images at a pixel level, in the way a human perceives it. Semantic Segmentation, which clusters parts of images together which belong to the same object on interest, solves this problem, and thus finds applications in myriad fields.

Note, that semantic segmentation is quite different and advanced compared to other image based tasks such as,

Image Classification identify what is present in the image.

Object Recognition (and Detection) identify what is present in the image and where (via a Bounding Box).

Semantic Segmentation identify what is present in the image and where (by finding all pixels that belong it).

Does your machine learning model needs to identify each and every pixel in the input 2D raw image? In such a case, full pixel semantic segmentation annotation is the key to your machine learning model. Full-pixel semantic segmentation assigns each pixel in an image is with a classID depending on which object of interest it belongs to.

Well let’s just define the types of semantic segmentation for understanding the concept better.

Types of Semantic Segmentation

Standard Semantic Segmentation also called full pixel semantic segmentation. It’s a process of classifying each pixel as belong to an object class.

Instance-aware Semantic Segmentation is a subtype of the standard semantic segmentation or full pixel semantic segmentation. It’s classifying each pixel as belong to an object class as well as entity ID for that class.

Let’s explore some application fields of semantic segmentation to get a better understanding of the need of such a process.

Features of Semantic segmentation

To understand the features of image segmentation, let’s also look at other common image classification techniques.

This time, I will introduce these three, including image segmentation.

1) Image classification … “Identify what the image is”

2) Image detection (identification) “Identify where in the image”

3) Image segmentation… ” Identify the meaning “

Image classification – This revolves around the idea of identifying what the image is. For example, it is a feeling that classified images of various sushi stories are classified one by one like “This is a salmon, how much is this, this is tough”. Object and scene detection ofAmazon Recognition recently released fromAmazon also belongs to this image classification.Originally “Cup and smartphone and bottle” are reflected, but Amazon Rekognition has come up with Cup and Coffee Cup as the labeling of the whole image.With this, it can not be used in scenes where multiple objects enter the image. In that case you will use “image detection (detection)”.

Image detection – This revolves around the idea of identifying “what is there” and “where it is”.

Image segmentation – This revolves around the idea of identifying the image region. Image segmentation called Semantic Segmentation labels the meaning indicated by that pixel for each pixel instead of detecting the entire image or part of the image. Since it is easier to see the image, let’s see the actual image.

Applications of Semantic Segmentation

GeoSensing – For land usage

Semantic Segmentation problems can also be considered classification problems, where each pixel is classified as one from a range of object classes. Thus, there is a use case for land usage mapping for satellite imagery. Land cover information is important for various applications, such as monitoring areas of deforestation and urbanization.

To recognize the type of land cover (e.g., areas of urban, agriculture, water, etc.) for each pixel on a satellite image, land cover classification can be regarded as a multi-class semantic segmentation task. Road and building detection is also an important research topic for traffic management, city planning, and road monitoring.

There are few large-scale publicly available datasets (Eg : SpaceNet), and data labeling is always a bottleneck for segmentation tasks.

For Autonomous driving

Autonomous driving is a complex robotics tasks that requires perception, planning and execution within constantly evolving environments. This task also needs to be performed with utmost precision, since safety is of paramount importance. Semantic Segmentation provides information about free space on the roads, as well as to detect lane markings and traffic signs.

Clothing parsing is a very complex task compared to others due to the large number of classes. This distinguishes itself from general object or scene segmentation problems since fine-grained clothing categorization requires higher-level judgment based on the semantics of clothing, variability of human-pose and the potentially large number of classes. Clothing parsing has been actively studied in the vision community because of its tremendous value in real-world applications i.e E-commerce.Some datasets such as Fashionista and CFPD datasets provide open access to semantic segmentation for clothing items.

Precision Agriculture

Precision farming robots can reduce the amount of herbicides that need to be sprayed out in the fields and semantic segmentation of crops and weeds assist them in real time to trigger weeding actions. Such advanced image vision techniques for agriculture can reduce manual monitoring of agriculture.

Do you have an automated way to pull out the right content from your archive, or are you relying on manual labor to watch each video and add metadata for each asset? As rich media content explodes – not just professionally produced long-form content, but also new digital-first content – metadata is key to describing […]

Do you have an automated way to pull out the right content from your archive, or are you relying on manual labor to watch each video and add metadata for each asset?

As rich media content explodes – not just professionally produced long-form content, but also new digital-first content – metadata is key to describing and categorizing digital content to provide better search visibility. Metadata directly affects your ability to search and find content efficiently. The challenge faced by content producers today is that existing metadata is basic and limited to only include title, cast, and synopsis. The problem is worse when you have many years’ worth of content that has never been tagged with metadata. Even if you can hire people to view archival material and add metadata, the metadata content often lacks quality and accuracy.

What if you could enrich the metadata by tagging your content with relevant data such as theme, tone, mood, trending data, and identification of things like make of cars and specific people included in the videos? Better metadata can help content producers find content quickly and accurately, so that they can deliver the right entertainment to the right viewers at the right time. Better metadata can also guide you in personalizing your services, thereby optimizing your content for specific audiences.

What if you could automate metadata creation with AI? By applying machine learning to rapidly analyze vast amounts of unstructured content and determine meaningful metadata to record, you can make your searches more specific. This will significantly improve your ability to find the right content when you need it.

AI relies on data to be powerful. The more metadata you have, the more AI can do to optimize your content. This is where the MapR Data Platform can help. MapR provides effective AI tools to construct machine learning models and a robust data infrastructure behind them to enable content producers to connect millions of people everyday to the entertainment they love.

Content from entertainment and media companies are measured in TBs and PBs today. All this unstructured content can be stored and managed on the MapR Data Platform. And when you use AI tools from the MapR Data Science Refinery to automatically generate more meaningful metadata, you can store, process, and analyze all existing and new content and metadata in a single data platform.

You might ask: how does MapR help me generate “meaningful” metadata? With built-in replicable and replayable event streaming, you can feed real-time, relevant data from internal and external data sources, such as social media and trending data, to your machine learning models to enrich metadata for your content. To ensure business continuity, we can replicate metadata alongside data to allow producers to failover between locations. Machine learning models are only as good as the data they are trained on. With MapR, data scientists can get access to all the data and allow their models to continually learn at scale, which can effectively make content more discoverable. Data scientists within your organization can leverage the MapR Platform’s global namespace, snapshots, and replication capabilities to efficiently share and collaborate on AI projects.

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Everyone wants to jump on the bandwagon of machine learning and leverage the diverse range of opportunities and careers this field promises. One question that arises against this backdrop relates to the choice of programming language. Online and offline, developers and programmers are debating on the pros and cons of each programming language’s compatibility with […]

Everyone wants to jump on the bandwagon of machine learning and leverage the diverse range of opportunities and careers this field promises. One question that arises against this backdrop relates to the choice of programming language. Online and offline, developers and programmers are debating on the pros and cons of each programming language’s compatibility with machine learning concepts. Two languages that have come at the top of this discussion are Python and R. This blog will discuss the role of both these languages in Machine learning.

Is There A Thing Such As Best Language For Machine Learning?

There are a lot of data available that suggests that popularity as a yardstick is not a good measure for choosing a programing language for machine learning. Instead users should consider other factors such as what they are going to build, their learning background and their primary purpose for getting involved in machine learning before they select a programming language. Most developers port the language that they were already using into machine learning, particularly when they are using it in projects alongside their previous project. However, for users whose foray into programming is through machine learning, popular opinion suggests that it is better to use Python which is easier to use.
Packages in Python & R

Python has several packages that make it ideal for machine learning purposes. PyBrain is modular machine learning library that provides powerful algorithms for machine learning purposes. The algorithm is flexible and intuits. The library also has different environments where the user can do comparison and testing of their machine learning algorithms. Scikit-learn is a library built on SciPy and NumPy and is the most popular python library for machine learning. NumPy and SciPy which are the core data analysis libraries are brought together in scikit-learn with a low entry barrier.

Like Python, R also possesses a lot of packages which enhances its use in machine learning. Nnet is one such library in machine learning that offers R the potential to model neural networks in an easy way. Yet another package that improves R’s machine learning capacity is Caret. With the functions enabled by Caret, a user can improve their efficiency of their predictive model development.

When To Choose Python or R for Machine Learning

Choose Python For Machine Learning….

When you are a beginner

Python is syntactically easier to start with as compared to other programming language though it has its own nuances. Through the years python’s data science and scientific stack has undergone fast transformation. There is a whole range of libraries in python fit for machine learning and data science. Many of these libraries are written in a lower-level language that makes use of a interface/wrapper that enables python to use it. All this would enable a beginner to concentrate more on machine learning concepts and not having to deal with other insignificant concerns.

When you want to develop/build Machine Learning products

In an engineering environment, python certainly integrates much better when compared to R. Though a user has to employ lower level languages like C, C++ or Java while writing an efficient code, offering it a python wrapper is a good step for enabling better integration with other elements.

Choose R For Machine Learning….

When want to explore the mathematics of ML

R is the best programming language for a user who is interested in the mathematical side of ML like statistical computing , statistics and the like. Its popularity in math is proven by the fact that most statisticians all over employ R. In fact, it is widely said that R is built by statisticians for statisticians. Its syntax might be daunting for some and even though R possesses a range of powerful ML packages, it is a bit scattered. However, it has a great package like Caret which tries to bring together several machine learning algorithms and related operations in a single interface.

When you want to do data visualization and analytics

R is the better choice amongst other programming languages including Python when it comes to rapid prototyping and using datasets for developing ML models.

Why Python is an ALL- Rounder!

If an individual does not know either of these languages, it is better to learn python. By learning python they can access the power of both the languages using python’s interface RPy2 which enables the user to access R’s functionality.

Python is also the best choice when it comes to professional choice. It is production ready: most enterprises has production systems for python, used widely in a diverse range of industries and also allows easy collaboration.

What is Artificial Intelligence? Artificial Intelligence is a concept that has been around for a while now. According to famous Greek mythologies, Artificial Intelligence was first discovered when mechanical men were designed in order to mimic the behavior of a human. After a few decades, when the first computers were introduced, they were conceived as […]

Artificial Intelligence is a concept that has been around for a while now. According to famous Greek mythologies, Artificial Intelligence was first discovered when mechanical men were designed in order to mimic the behavior of a human. After a few decades, when the first computers were introduced, they were conceived as something of a logical machine that had the capabilities of reproducing memory, arithmetic operations, and logical operations and so on and so forth.

In today’s day and age, Artificial Intelligence has somewhat of a deeper meaning in the sense that it uses the understanding of the human mind and the progression of complex calculations done by the brain into a machine.

There are mainly two classifications with respect to Artificial Intelligence. These are:

– Generalized AIs: These are systems or devices that are less commonly used today. They, in theory, can handle any tasks and are at the core of most advancement in the field that is happening. These machines are what led to the concept of Machine Learning.

– Applied AIs: These are far more common and complex systems that are designed to intelligently perform functions such as the trading of stocks and shares, manoeuvring of vehicles etc.

What is Machine Learning?

Machine Learning, a concept that has gained considerable momentum in the recent past, is, in fact, an application or genre of Artificial Intelligence. Thus, it provides systems or devices that are programmed on the basis of AI, the ability to learn automatically and improvise on whatever has been learned without actually being explicitly programmed. Thus, it focuses on the development of a form of self-sufficient computer programs and devices which can access data learn from it and use it, all by themselves.

It is done by first helping the system to observe data through direct or indirect means. This can be done either by determining patterns in behavior or instructions. Based on these observations, decisions with respect to future functioning are taken. The main advantage of such a system is that there is little or no human intervention and the system adjusts itself accordingly to the need of the hour.

How is Machine Learning different from Artificial Intelligence?

There are some stand-out differences between Artificial Intelligence and Machine Learning. These differences are mostly application based, considering that Machine Learning is one of the applications of Artificial Intelligence. Some of these differences are:

– AI defines intelligence as the acquisition of knowledge. Thus, Artificial Intelligent focuses on the ability to acquire as well as apply knowledge. On the other hand, Machine learning treats knowledge as a self-sufficient skill that needs no human intervention.

– Artificial Intelligence focuses more on the success rates of devices and systems rather than the accuracy. However, Machine Learning may give lesser success rates, but the accuracy is always higher than that of AI.

– Artificial Intelligence can be compared to a computer program that is already pre-written and follows certain protocols that help with smart work. Machine Learning, is a concept machine that acquires data through certain protocols and learns from the data acquired.

– The goal of Artificial Intelligence is to solve any complex problem or algorithm by using concepts of natural intelligence. On the other hand, the goal of Machine Learning is to learn from the data acquired in order to maximize the performance of the machine that is performing on a certain task.

– While Artificial Intelligence is on the path to mimic human responses to circumstances and problems, Machine Learning focuses on self-learning algorithms which in turn will increase the reliability of the machine or device that is being used.

The structure is close to those of the famous neural networks: the idea is to mimic the human brain, which is known to be very efficient in learning. A large number of layers with nonlinear processes between them are used: the deeper the network is, the more complex structures it can capture. The first machine […]

The structure is close to those of the famous neural networks: the idea is to mimic the human brain, which is known to be very efficient in learning. A large number of layers with nonlinear processes between them are used: the deeper the network is, the more complex structures it can capture. The first machine learningalgorithms appeared in the 1950’s and their development is clearly related to computational-power improvements.

About Deep learning

Deep Learning has proven its ability to solve many different problems from handwriting and speech recognition to computer vision. The structure of the algorithms is based on a reproduction of the human brain, which is known to be the most powerful engine. It is able to capture the latent structure in any dataset as a human being could and the results seem somehow magical for someone who is not familiar with this class of algorithms. The main purpose of this paper is to test its limits. After a great success in Go, the next step is simply to test whether deep learning is able to deal with randomness. It looks feasible because God does not play dice, Go is indeed a pure combinatorial problem and it may merely be reduced to a computational and optimization task. Randomness is conceptually more interesting and cannot be reduced to few dimensions: a higher dimensional model is required.

Lotto and its working

Lotto is a famous and widespread game involving randomness. The first lotteries are not clearly established and the Han dynasty used it to finance the construction of the Great Wall of China. The lottery principle is very easy: people buy a ticket which corresponds to a combination bet over a general set of numbers. A draw is eventually done at a fixed date and time. The gains are related to how the combination matches the draw and the jackpot is won if the combination is correct.

Predicting Lotto numbers is a supervised task: the collected data, in the present case based on the past draws, are used as inputs. The model is a neural network whose parameters are tuned according to the data during the training phase.Training is often difficult in neural networks, due to vanishing or exploding gradients. This is the main problem in these algorithms. At each pass over the data, the parameters are optimized and, after convergence, the validation set is used to compute the validation error.

Model and its representation

The features retained are firstly, at each draw time, the quarterly GDP, the quarterly unemployment rate, the American President (Obama or not), the day, the month and the year. To this, I added the number of times each number was drawn during all past draws and the cross presence matrix defined as the number of times every pair of numbers appeared together. For the number of times each number was drawn and the cross presence matrix, they were set to zero for the first draw and then were incremented at each step. The neural network implemented is represented figure below. I distinguished the cross-presence matrix and the other inputs. I applied convolutional layers to the cross-presence matrix. Then, using residual learning, I added the intermediate result to the output of the convolutional layers.

This is concatenated with all other features (quarterly GDP, unemployment rate, American president, day, month, year, and number of times every number was seen) and acts as an input to a first dense layer. A second dense layer leads to the final prediction. A non-linear sigmoid is used to predict the presence or not of the lotto number. For instance, on figure below, the 2 and 46 are two numbers (out of the six numbers) that are predicted given the input.

The output loss chosen was the categorical cross-entropy between predictions and targets. For 2 one data point k, the categorical cross-entropy is:

Equation

With N being the number of categories (number of possible numbers, 75), pk, the target distribution and qk the prediction from the neural network. To obtain the overall categorical cross-entropy, I average over all data points. The optimizer used was Adam. I split the set of observations into a training set of 892 draws and a validation set of 315 draws.

Representation of a Deep Neural Network Model

Result

The results are plotted in the graphical below. The graph on the left is the error on the training set. To check for overfitting, I also calculated the error on the validation set. On both sets, the error goes down substantially, dividing the initial error by 5. This is the proof that it is a capturing an unidentified structure underlying the data. I would like to emphasize this point: even though the neural network in my brain can not identify the underlying structure of the data, the liberties given to the deep neural network give the possibility to learn a larger class of functions which explains how this model could capture an understanding of loto when the human brain can only interpret it, at best, as randomness. Moreover, the algorithm converges quickly after only a few iterations showing the efficiency of the neural network.

Result Analysis and Discussion

Graphs showing Training and Validation error

Following the logic of the results, this leads to a new understanding of the concept of randomness. Where the human brain essentially understands randomness, a powerful model from the neural network framework captures a non-random structure. The human brain, as a physical system, has limits and the deep learning framework also. What all represented here is that the human brain limits are contained strictly within the deep learning limits which leads to whole new possibilities on our understanding of the world and to all the remaining unanswered questions.

Conclusion

For a large-scale proof of concept, I predicted the numbers that will be drawn on the 11th of April, these will be 1, 9, 13, 14, 63, and the mega number will be 7. And I can conclude on the existence of God.

When it comes to software development, there are two kinds of development. There’s front end and back end development. Back end development refers to what goes on, “under the hood” so to speak. It’s everything that the user of the application or website doesn’t see. By contrast, front end development consists of everything that the […]

When it comes to software development, there are two kinds of development. There’s front end and back end development. Back end development refers to what goes on, “under the hood” so to speak. It’s everything that the user of the application or website doesn’t see. By contrast, front end development consists of everything that the user sees and interacts with. Front end development can include webpage design, and for years now web pages have been created by a web designer who uses a variety of tools kits, some simpler than others. Recent advancements in deep learning are making it possible to auto-generate web pages and websites, making the process of creating a website much easier and quicker.

The ability for a neural network to auto generate a webpage comes from the synthesis of various other AI abilities. Neural networks have been able to automatically recognize images and generate language to limited degrees for a while now, and the combination of these traits can enable the generation of web pages. Instead of a single image being passed into a neural network, a proposed layout, grid, or wireframe of the webpage is passed into the network via a screenshot with HTML tags. The network is capable of recognizing discrete elements of the web page, such as toolbar, image areas, and text fields. The network is able to interpret this layout and fill it in with the requisite HTML and CSS code that makes the website useable to the average person.

HTML code is what is used to create the structure or framework of a website. It is what indicates which elements and aspects of a website go where. When you see a website with links, headers, text fields, images, comment sections and search functions, the layout and function is all handled by HTML code. CSS code is what defines how the HTML code looks, or is presented to the user. The CSS code defines things like font size and shape, style elements.

A blog post written by Emil Wallner at FloydHub Blog broke down the process of generating code from design mockups into several different steps. The neural network interprets the general structure of the website from the inputted layout, and then fills in the structure of the page with the HTML code. One thing to take note of is the fact that the neural network doesn’t just construct the HTML code from scratch, it initially based its code on the patterns and conventions used by a simple website designed to display Hello World. The model predicted the the matching HTML tags word by word, one by one. After approximately 300 epochs of training, the network reproduced a simple Hello World webpage.

Deep Learning is used by Google in its voice and image recognition algorithms, by Netflix and Amazon to decide what you want to watch or buy next, and by researchers at MIT to predict the future.

Moving on to a more complex task, Wallner scaled up the general algorithms employed in the Hello World model to successfully create a dummy website filled in with dummy images and text. This was done by delineating features of the markup by using word embedding for the input and one-hot encoding for the network’s output. The word embeddings were then put through a Long Short Term Memory layer. The images were prepared for use at the same time by transforming the individual image features into one continuous list. The images and text for the website were then concatenated together. A decoder was used to predict the next tag in the sequence once the image-markup features were combined.

In the final portion of Wallner’s experiment, websites were generate with the Bootstrap web development framework were used for the dataset. Bootstrap is a front end framework that lets web developers create things out of pre-established modules, sort of like using building blocks to create the website. The neural network examines the features of the layout and then maps the features to the modules which are part of Bootstrap. This meant that CSS and HTML could be now be combined and used together, and that the size of the vocabulary could be decreased.

Once more, a LSTM was used to fill the role of a recurrent neural network and enable the system to predict things beyond a couple timesteps, which was necessary for the network to maintain information about aspects of the front end design like position and color of text and images. The result was an auto generated web page that almost perfectly mimicked the layout specified by Bootstrap.

Sophisticated applications of deep learning systems allows the front end of websites to be created with less effort, potentially freeing up resources and allowing developers to focus on other aspects of web development.

[purchase_link id=”5642″ style=”button” color=”blue” text=”Download TDWI Report”] In this report, the group currently using the technology is referred to as the active group. Those who are planning to use the technology are referred to as the exploring group. In this survey, half (50%) already use the technology; more than half of this active group has […]

In this report, the group currently using the technology is referred to as the active group. Those who are planning to use the technology are referred to as the exploring group. In this survey, half (50%) already use the technology; more than half of this active group has been using it for more than three years. Forty percent are exploring the technology now. Ten percent are not using the technology. This should not be viewed as an adoption rate because respondents tend to gravitate to surveys they can relate to.

The majority of active group enterprises have models in production. Models provide the most value when used in production to make decisions and take action. The majority of active group respondents (73%, not shown) already have models in production. Another 17% plan to have models in production in the next few months. About 10% either don’t have models in production or don’t know when they might. The fact that the vast majority have put models into production is good news and illustrates that this group of respondents realizes the value of taking action on analytics. In fact, operationalizing models (to make them part of a business process) is one of the top areas of interest for moving programs forward. It is also important for making predictive analytics more pervasive.

Predictive analytics is used across a range of use cases. We asked both the active group and the exploring group what kinds of use cases they are using or plan to use in predictive analytics. As illustrated in Figure 1, there are many popular use cases for predictive analytics for both groups.

What is Predictive Analytics? Predictive analytics consists of statistical and machine learning algorithms used to determine the probability of future outcomes using historical data. Some people think of machine learning as being completely different from predictive analytics. Machine learning techniques, however, are often used in predictive analytics; they just use a different approach.

• Marketing applications often lead the way. Over half (52%) of the active group is using predictive analytics for retention analysis or direct marketing. Cross-sell and up-sell is also popular in marketing. These are popular use cases for the exploring group as well with a third of respondents planning to deploy these use cases in the short term. Previous TDWI research indicates that marketing is often one of the first departments to adopt more advanced analytics.

• Default prediction is also popular. In addition to marketing use cases, respondents are also utilizing or interested in other kinds of use cases. For example, default prediction ranked high for both groups with 46% of the active group already doing this kind of analysis and 34% of the exploring group planning to do so in the short term. Default analysis is important for a number of use cases including loans, credit cards, premium payments, and tuition payments.

• Newer use cases such as predictive maintenance are gaining steam. Thirty-four percent of the active group is already using predictive maintenance and 22% of the exploratory group plans to use it. In predictive maintenance, organizations calculate the probability of an operational asset requiring servicing or even failing. Some organizations make use of sensor data from the Internet of Things (IoT). For instance, a fleet operator might use sensors to collect data from their various trucks. Such data might include the temperature or number of vibrations per second of a particular part or parts. This data can be analyzed using machine learning to determine what precipitates a part failure or when undue wear and tear is occurring. The system “learns” the patterns that constitute the need for repair. That information might be encoded into a set of rules or a model and used to score new data from trucks in order to improve fleet maintenance and operational efficiency. Other use cases include cybersecurity, where 25% of the active group is using predictive analytics (not shown).

What is Machine Learning? Machine learning methods originated in the field of computational science a few decades ago. In machine learning, systems learn from data to identify patterns with minimal human intervention. The computer learns from examples, typically using either supervised or unsupervised approaches. In supervised learning, the system is given a target (also known as an output or label) of interest. The system is trained on these outcomes using various attributes (also called features). In unsupervised learning, there are no outcomes specified.

• In applications. An up-and-coming area of interest is embedding predictive analytics and machine learning models in applications that require intelligence. We did not ask specifically about these applications but they are worth mentioning here. These include voice recognition, traffic apps, and chatbots. Although only 12% (not shown) cite building apps as a driver for predictive analytics and machine learning, we expect that percentage
to grow.